OpenRaiser/Intern-Atlas huggingface dataset:
Unsupervised Dense Shape Correspondence using Heat Kernels
Venue: arXiv cs.CV 2020
Year: 2020

In this work, we propose an unsupervised method for learning dense correspondences between shapes using a recent deep functional map framework. Instead of depending on ground-truth correspondences or the computationally expensive geodesic distances, we use heat kernels. These can be computed quickly during training as the supervisor signal. Moreover, we propose a curriculum learning strategy using different heat diffusion times which provide different levels of difficulty during optimization without any sampling mechanism or hard example mining. We present the results of our method on different benchmarks which have various challenges like partiality, topological noise and different connectivity.
Long Document Ranking with Query-Directed Sparse Transformer
Venue: arXiv cs.AI 2020
Year: 2020

The computing cost of transformer self-attention often necessitates breaking long documents to fit in pretrained models in document ranking tasks. In this paper, we design Query-Directed Sparse attention that induces IR-axiomatic structures in transformer self-attention. Our model, QDS-Transformer, enforces the principle properties desired in ranking: local contextualization, hierarchical representation, and query-oriented proximity matching, while it also enjoys efficiency from sparsity. Experiments on one fully supervised and three few-shot TREC document ranking benchmarks demonstrate the consistent and robust advantage of QDS-Transformer over previous approaches, as they either retrofit long documents into BERT or use sparse attention without emphasizing IR principles. We further quantify the computing complexity and demonstrates that our sparse attention with TVM implementation is twice more efficient than the fully-connected self-attention. All source codes, trained model, and predictions of this work are available at https://github.com/hallogameboy/QDS-Transformer.
Stabilizing Transformer-Based Action Sequence Generation For Q-Learning
Venue: arXiv cs.LG 2020
Year: 2020

Since the publication of the original Transformer architecture (Vaswani et al. 2017), Transformers revolutionized the field of Natural Language Processing. This, mainly due to their ability to understand timely dependencies better than competing RNN-based architectures. Surprisingly, this architecture change does not affect the field of Reinforcement Learning (RL), even though RNNs are quite popular in RL, and time dependencies are very common in RL. Recently, Parisotto et al. 2019) conducted the first promising research of Transformers in RL. To support the findings of this work, this paper seeks to provide an additional example of a Transformer-based RL method. Specifically, the goal is a simple Transformer-based Deep Q-Learning method that is stable over several environments. Due to the unstable nature of Transformers and RL, an extensive method search was conducted to arrive at a final method that leverages developments around Transformers as well as Q-learning. The proposed method can match the performance of classic Q-learning on control environments while showing potential on some selected Atari benchmarks. Furthermore, it was critically evaluated to give additional insights into the relation between Transformers and RL.
LCFI: A Fault Injection Tool for Studying Lossy Compression Error Propagation in HPC Programs
Venue: arXiv cs.DC 2020
Year: 2020

Error-bounded lossy compression is becoming more and more important to today's extreme-scale HPC applications because of the ever-increasing volume of data generated because it has been widely used in in-situ visualization, data stream intensity reduction, storage reduction, I/O performance improvement, checkpoint/restart acceleration, memory footprint reduction, etc. Although many works have optimized ratio, quality, and performance for different error-bounded lossy compressors, there is none of the existing works attempting to systematically understand the impact of lossy compression errors on HPC application due to error propagation. In this paper, we propose and develop a lossy compression fault injection tool, called LCFI. To the best of our knowledge, this is the first fault injection tool that helps both lossy compressor developers and users to systematically and comprehensively understand the impact of lossy compression errors on HPC programs. The contributions of this work are threefold: (1) We propose an efficient approach to inject lossy compression errors according to a statistical analysis of compression errors for different state-of-the-art compressors. (2) We build a fault injector which is highly applicable, customizable, easy-to-use in generating top-down comprehensive results, and demonstrate the use of LCFI. (3) We evaluate LCFI on four representative HPC benchmarks with different abstracted fault models and make several observations about error propagation and their impacts on program outputs.
Mechanism Design for Stable Matching with Contracts in a Dynamic Manufacturing-as-a-Service (MaaS) Marketplace
Venue: arXiv cs.GT 2020
Year: 2020

Two-sided manufacturing-as-a-service (MaaS) marketplaces connect clients requesting manufacturing services to suppliers providing those services. Matching mechanisms i.e. allocation of clients' orders to suppliers is a key design parameter of the marketplace platform. The platform might perform an allocation to maximize its revenue or optimize for social welfare of all participants. However, individual participants might not get maximum value from their match and reject it to form matches (called blocking groups) themselves, thereby bypassing the platform. This paper considers the bipartite matching problem in MaaS marketplaces in a dynamic environment and proposes approximately stable matching solutions using mechanism design and mathematical programming approaches to limit the formation of blocking groups. Matching is based on non-strict, incomplete and interdependent preferences of participants over contracts enabling negotiations between both sides. Empirical simulations are used to test the mechanisms in a simulated 3D printing marketplace and to evaluate the impact of stability on its performance. It is found that stable matching results in small degradation in social welfare of the marketplace. However, it leads to a significantly better outcome in terms of stability of allocation. Unstable matchings introduce anarchy into marketplace with participants rejecting its allocation leading to performance poorer than stable matchings.
Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering
Venue: arXiv cs.IR 2020
Year: 2020

Most existing One-Class Collaborative Filtering (OC-CF) algorithms estimate a user's preference as a latent vector by encoding their historical interactions. However, users often show diverse interests, which significantly increases the learning difficulty. In order to capture multifaceted user preferences, existing recommender systems either increase the encoding complexity or extend the latent representation dimension. Unfortunately, these changes inevitably lead to increased training difficulty and exacerbate scalability issues. In this paper, we propose a novel and efficient CF framework called Attentive Multi-modal AutoRec (AMA) that explicitly tracks multiple facets of user preferences. Specifically, we extend the Autoencoding-based recommender AutoRec to learn user preferences with multi-modal latent representations, where each mode captures one facet of a user's preferences. By leveraging the attention mechanism, each observed interaction can have different contributions to the preference facets. Through extensive experiments on three real-world datasets, we show that AMA is competitive with state-of-the-art models under the OC-CF setting. Also, we demonstrate how the proposed model improves interpretability by providing explanations using the attention mechanism.
Pressure Mode Decomposition Analysis of the Flow past a Cross-flow Oscillating Circular Cylinder
Venue: arXiv cs.CE 2020
Year: 2020

Proper orthogonal decomposition (POD) is often employed in developing reduced-order models (ROM) in fluid flows for design, control, and optimization. Contrary to the usual practice where velocity field is the focus, we apply the POD analysis on the pressure field data obtained from numerical simulations of the flow past stationary and oscillating cylinders. Since pressure mainly contributes to the hydrodynamic forces acting on the structure, we compute the pressure POD modes on the cylinder surface oscillating in lock-in and lock-out regions. These modes are then dissected into sine and cosine magnitudes to estimate their contribution in the development of pressure lift and drag decomposition coefficients, respectively. The key finding of this study is that more POD modes are required to capture the flow physics in nonsynchronous regimes as compared to synchronization case. Engineering application of this study is the development of reduced-order models for effective control techniques.
Safeguarding the IoT from Malware Epidemics: A Percolation Theory Approach
Venue: arXiv cs.CR 2020
Year: 2020

The upcoming Internet of things (IoT) is foreseen to encompass massive numbers of connected devices, smart objects, and cyber-physical systems. Due to the large-scale and massive deployment of devices, it is deemed infeasible to safeguard 100% of the devices with state-of-the-art security countermeasures. Hence, large-scale IoT has inevitable loopholes for network intrusion and malware infiltration. Even worse, exploiting the high density of devices and direct wireless connectivity, malware infection can stealthily propagate through susceptible (i.e., unsecured) devices and form an epidemic outbreak without being noticed to security administration. A malware outbreak enables adversaries to compromise large population of devices, which can be exploited to launch versatile cyber and physical malicious attacks. In this context, we utilize spatial firewalls, to safeguard the IoT from malware outbreak. In particular, spatial firewalls are computationally capable devices equipped with state-of-the-art security and anti-malware programs that are spatially deployed across the network to filter the wireless traffic in order to detect and thwart malware propagation. Using tools from percolation theory, we prove that there exists a critical density of spatial firewalls beyond which malware outbreak is impossible. This, in turns, safeguards the IoT from malware epidemics regardless of the infection/treatment rates. To this end, a tractable upper bound for the critical density of spatial firewalls is obtained. Furthermore, we characterize the relative communications ranges of the spatial firewalls and IoT devices to ensure secure network connectivity. The percentage of devices secured by the firewalls is also characterized.
Force and state-feedback control for robots with non-collocated environmental and actuator forces
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, we present an impedance control design for multi-variable linear and nonlinear robotic systems. The control design considers force and state feedback to improve the performance of the closed loop. Simultaneous feedback of forces and states allows the controller for an extra degree of freedom to approximate the desired impedance port behaviour. A numerical analysis is used to demonstrate the desired impedance closed-loop behaviour.
Abduction and Argumentation for Explainable Machine Learning: A Position Survey
Venue: arXiv cs.AI 2020
Year: 2020

This paper presents Abduction and Argumentation as two principled forms for reasoning, and fleshes out the fundamental role that they can play within Machine Learning. It reviews the state-of-the-art work over the past few decades on the link of these two reasoning forms with machine learning work, and from this it elaborates on how the explanation-generating role of Abduction and Argumentation makes them naturally-fitting mechanisms for the development of Explainable Machine Learning and AI systems. Abduction contributes towards this goal by facilitating learning through the transformation, preparation, and homogenization of data. Argumentation, as a conservative extension of classical deductive reasoning, offers a flexible prediction and coverage mechanism for learning -- an associated target language for learned knowledge -- that explicitly acknowledges the need to deal, in the context of learning, with uncertain, incomplete and inconsistent data that are incompatible with any classically-represented logical theory.
ATRO: Adversarial Training with a Rejection Option
Venue: arXiv cs.LG 2020
Year: 2020

This paper proposes a classification framework with a rejection option to mitigate the performance deterioration caused by adversarial examples. While recent machine learning algorithms achieve high prediction performance, they are empirically vulnerable to adversarial examples, which are slightly perturbed data samples that are wrongly classified. In real-world applications, adversarial attacks using such adversarial examples could cause serious problems. To this end, various methods are proposed to obtain a classifier that is robust against adversarial examples. Adversarial training is one of them, which trains a classifier to minimize the worst-case loss under adversarial attacks. In this paper, in order to acquire a more reliable classifier against adversarial attacks, we propose the method of Adversarial Training with a Rejection Option (ATRO). Applying the adversarial training objective to both a classifier and a rejection function simultaneously, classifiers trained by ATRO can choose to abstain from classification when it has insufficient confidence to classify a test data point. We examine the feasibility of the framework using the surrogate maximum hinge loss and establish a generalization bound for linear models. Furthermore, we empirically confirmed the effectiveness of ATRO using various models and real-world datasets.
Drawing Two Posets
Venue: arXiv cs.CG 2020
Year: 2020

We investigate the problem of drawing two posets of the same ground set so that one is drawn from left to right and the other one is drawn from the bottom up. The input to this problem is a directed graph $G = (V, E)$ and two sets $X, Y$ with $X \cup Y = E$, each of which can be interpreted as a partial order of $V$. The task is to find a planar drawing of $G$ such that each directed edge in $X$ is drawn as an $x$-monotone edge, and each directed edge in $Y$ is drawn as a $y$-monotone edge. Such a drawing is called an $xy$-planar drawing. Testing whether a graph admits an $xy$-planar drawing is NP-complete in general. We consider the case that the planar embedding of $G$ is fixed and the subgraph of $G$ induced by the edges in $Y$ is a connected spanning subgraph of $G$ whose upward embedding is fixed. For this case we present a linear-time algorithm that determines whether $G$ admits an $xy$-planar drawing and, if so, produces an $xy$-planar polyline drawing with at most three bends per edge.
Comments on "Precoding and Artificial Noise Design for Cognitive MIMOME Wiretap Channels"
Venue: arXiv cs.IT 2020
Year: 2020

Several gaps and errors in [1] are identified and corrected. While accommodating these corrections, a rigours proof is given that the successive convex approximation algorithm in [1] for secrecy rate maximization (SRM) does generate an increasing and bounded sequence of true secrecy rates and hence converges. It is further shown that its convergence point is a KKT point of the original SRM problem and, if the original problem is convex, this convergence point is globally-optimal, which is not necessarily the case in general. An interlacing property of the sequences of the true and approximate secrecy rates is established.
Road Accident Proneness Indicator Based On Time, Weather And Location Specificity Using Graph Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we present a novel approach to identify the Spatio-temporal and environmental features that influence the safety of a road and predict its accident proneness based on these features. A total of 14 features were compiled based on Time, Weather, and Location (TWL) specificity along a road. To determine the influence each of the 14 features carries, a sensitivity study was performed using Principal Component Analysis. Using the locations of accident warnings, a Safety Index was developed to quantify how accident-prone a particular road is. We implement a novel approach to predict the Safety Index of a road-based on its TWL specificity by using a Graph Neural Network (GNN) architecture. The proposed architecture is uniquely suited for this application due to its ability to capture the complexities of the inherent nonlinear interlinking in a vast feature space. We employed a GNN to emulate the TWL feature vectors as individual nodes which were interlinked vis-à-vis edges of a graph. This model was verified to perform better than Logistic Regression, simple Feed-Forward Neural Networks, and even Long Short Term Memory (LSTM) Neural Networks. We validated our approach on a data set containing the alert locations along the routes of inter-state buses. The results achieved through this GNN architecture, using a TWL input feature space proved to be more feasible than the other predictive models, having reached a peak accuracy of 65%.
Improved Actor Relation Graph based Group Activity Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Video understanding is to recognize and classify different actions or activities appearing in the video. A lot of previous work, such as video captioning, has shown promising performance in producing general video understanding. However, it is still challenging to generate a fine-grained description of human actions and their interactions using state-of-the-art video captioning techniques. The detailed description of human actions and group activities is essential information, which can be used in real-time CCTV video surveillance, health care, sports video analysis, etc. This study proposes a video understanding method that mainly focused on group activity recognition by learning the pair-wise actor appearance similarity and actor positions. We propose to use Normalized cross-correlation (NCC) and the sum of absolute differences (SAD) to calculate the pair-wise appearance similarity and build the actor relationship graph to allow the graph convolution network to learn how to classify group activities. We also propose to use MobileNet as the backbone to extract features from each video frame. A visualization model is further introduced to visualize each input video frame with predicted bounding boxes on each human object and predict individual action and collective activity.
Classification of Spot-welded Joints in Laser Thermography Data using Convolutional Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

Spot welding is a crucial process step in various industries. However, classification of spot welding quality is still a tedious process due to the complexity and sensitivity of the test material, which drain conventional approaches to its limits. In this paper, we propose an approach for quality inspection of spot weldings using images from laser thermography data.We propose data preparation approaches based on the underlying physics of spot welded joints, heated with pulsed laser thermography by analyzing the intensity over time and derive dedicated data filters to generate training datasets. Subsequently, we utilize convolutional neural networks to classify weld quality and compare the performance of different models against each other. We achieve competitive results in terms of classifying the different welding quality classes compared to traditional approaches, reaching an accuracy of more than 95 percent. Finally, we explore the effect of different augmentation methods.
The Benefits of Deploying Smart Contracts on Trusted Third Parties
Venue: arXiv cs.NI 2020
Year: 2020

The hype about Bitcoin has overrated the potential of smart contracts deployed on-blockchains (on-chains) and underrated the potential of smart contracts deployed on-Trusted Third Parties (on-TTPs). As a result, current research and development in this field is focused mainly on smart contract applications that use on-chain smart contracts. We argue that there is a large class of smart contract applications where on-TTP smart contracts are a better alternative. The problem with on-chain smart contracts is that the fully decentralised model and indelible append-only data model followed by blockchains introduces several engineering problems that are hard to solve. In these situations, the inclusion of a TTP (assuming that the application can tolerate its inconveniences) instead of a blockchain to host the smart contract simplifies the problems and offers pragmatic solutions. The intention and contribution of this paper is to shed some light on this issue. We use a hypothetical use case of a car insurance application to illustrate technical problems that are easier to solve with on-TTP smart contracts than with on-chain smart contracts.
APB2FaceV2: Real-Time Audio-Guided Multi-Face Reenactment
Venue: arXiv cs.CV 2020
Year: 2020

Audio-guided face reenactment aims to generate a photorealistic face that has matched facial expression with the input audio. However, current methods can only reenact a special person once the model is trained or need extra operations such as 3D rendering and image post-fusion on the premise of generating vivid faces. To solve the above challenge, we propose a novel \emph{R}eal-time \emph{A}udio-guided \emph{M}ulti-face reenactment approach named \emph{APB2FaceV2}, which can reenact different target faces among multiple persons with corresponding reference face and drive audio signal as inputs. Enabling the model to be trained end-to-end and have a faster speed, we design a novel module named Adaptive Convolution (AdaConv) to infuse audio information into the network, as well as adopt a lightweight network as our backbone so that the network can run in real time on CPU and GPU. Comparison experiments prove the superiority of our approach than existing state-of-the-art methods, and further experiments demonstrate that our method is efficient and flexible for practical applications https://github.com/zhangzjn/APB2FaceV2
The Smoothed Complexity of Computing Kemeny and Slater Rankings
Venue: arXiv cs.GT 2020
Year: 2020

The computational complexity of winner determination under common voting rules is a classical and fundamental topic in the field of computational social choice. Previous work has established the NP-hardness of winner determination under some commonly-studied voting rules, especially the Kemeny rule and the Slater rule. In a recent blue-sky paper, Baumeister, Hogrebe, and Rothe (2020) questioned the relevance of the worst-case nature of NP-hardness in social choice and proposed to conduct smoothed complexity analysis (Spielman and Teng 2009) under Blaser and Manthey (2015)'s framework. In this paper, we develop the first smoothed complexity results for winner determination in voting. We illustrate the inappropriateness of Blaser and Manthey (2015)'s smoothed complexity framework in social choice contexts by proving a paradoxical result, which states that the exponential-time brute force search algorithm is smoothed poly-time according to their definition. We then prove the smoothed hardness of Kemeny and Slater using the classical smoothed complexity analysis, and prove a parameterized typical-case smoothed easiness result for Kemeny. Overall, our results show that smoothed complexity analysis in computational social choice is a challenging and fruitful topic.